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electrocardiogram signals</title> <meta name="description" content="Search results for: electrocardiogram signals"> <meta name="keywords" content="electrocardiogram signals"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="electrocardiogram signals"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1034</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: electrocardiogram signals</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1034</span> Electrocardiogram Signal Denoising Using a Hybrid Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Latif">R. Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Jenkal"> W. Jenkal</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Toumanari"> A. Toumanari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hatim"> A. Hatim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20technique" title="hybrid technique">hybrid technique</a>, <a href="https://publications.waset.org/abstracts/search?q=ADTF" title=" ADTF"> ADTF</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a> </p> <a href="https://publications.waset.org/abstracts/65458/electrocardiogram-signal-denoising-using-a-hybrid-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1033</span> Wavelet-Based Classification of Myocardial Ischemia, Arrhythmia, Congestive Heart Failure and Sleep Apnea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santanu%20Chattopadhyay">Santanu Chattopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Gautam%20Sarkar"> Gautam Sarkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabinda%20Das"> Arabinda Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmia" title="arrhythmia">arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=congestive%20heart%20failure" title=" congestive heart failure"> congestive heart failure</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20ischemia" title=" myocardial ischemia"> myocardial ischemia</a>, <a href="https://publications.waset.org/abstracts/search?q=sleep%20apnea" title=" sleep apnea"> sleep apnea</a> </p> <a href="https://publications.waset.org/abstracts/112333/wavelet-based-classification-of-myocardial-ischemia-arrhythmia-congestive-heart-failure-and-sleep-apnea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112333.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1032</span> Electrocardiogram-Based Heartbeat Classification Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jacqueline%20Rose%20T.%20Alipo-on">Jacqueline Rose T. Alipo-on</a>, <a href="https://publications.waset.org/abstracts/search?q=Francesca%20Isabelle%20F.%20Escobar"> Francesca Isabelle F. Escobar</a>, <a href="https://publications.waset.org/abstracts/search?q=Myles%20Joshua%20T.%20Tan"> Myles Joshua T. Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hezerul%20Abdul%20Karim"> Hezerul Abdul Karim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nouar%20Al%20Dahoul"> Nouar Al Dahoul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heartbeat%20classification" title="heartbeat classification">heartbeat classification</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20signals" title=" electrocardiogram signals"> electrocardiogram signals</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet-50" title=" ResNet-50"> ResNet-50</a> </p> <a href="https://publications.waset.org/abstracts/162763/electrocardiogram-based-heartbeat-classification-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162763.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1031</span> Remote Wireless Patient Monitoring System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagar%20R.%20Patil">Sagar R. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinesh%20R.%20Gawade"> Dinesh R. Gawade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhir%20N.%20Divekar"> Sudhir N. Divekar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the medical devices we found when we visit a hospital care unit such device is ‘patient monitoring system’. This device (patient monitoring system) informs doctors and nurses about the patient’s physiological signals. However, this device (patient monitoring system) does not have a remote monitoring capability, which is necessitates constant onsite attendance by support personnel (doctors and nurses). Thus, we have developed a Remote Wireless Patient Monitoring System using some biomedical sensors and Android OS, which is a portable patient monitoring. This device(Remote Wireless Patient Monitoring System) monitors the biomedical signals of patients in real time and sends them to remote stations (doctors and nurse’s android Smartphone and web) for display and with alerts when necessary. Wireless Patient Monitoring System different from conventional device (Patient Monitoring system) in two aspects: First its wireless communication capability allows physiological signals to be monitored remotely and second, it is portable so patients can move while there biomedical signals are being monitor. Wireless Patient Monitoring is also notable because of its implementation. We are integrated four sensors such as pulse oximeter (SPO2), thermometer, respiration, blood pressure (BP), heart rate and electrocardiogram (ECG) in this device (Wireless Patient Monitoring System) and Monitoring and communication applications are implemented on the Android OS using threads, which facilitate the stable and timely manipulation of signals and the appropriate sharing of resources. The biomedical data will be display on android smart phone as well as on web Using web server and database system we can share these physiological signals with remote place medical personnel’s or with any where in the world medical personnel’s. We verified that the multitasking implementation used in the system was suitable for patient monitoring and for other Healthcare applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=patient%20monitoring" title="patient monitoring">patient monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20patient%20monitoring" title=" wireless patient monitoring"> wireless patient monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-medical%20signals" title=" bio-medical signals"> bio-medical signals</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signals" title=" physiological signals"> physiological signals</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20system" title=" embedded system"> embedded system</a>, <a href="https://publications.waset.org/abstracts/search?q=Android%20OS" title=" Android OS"> Android OS</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20oximeter%20%28SPO2%29" title=" pulse oximeter (SPO2)"> pulse oximeter (SPO2)</a>, <a href="https://publications.waset.org/abstracts/search?q=thermometer" title=" thermometer"> thermometer</a>, <a href="https://publications.waset.org/abstracts/search?q=respiration" title=" respiration"> respiration</a>, <a href="https://publications.waset.org/abstracts/search?q=blood%20pressure%20%28BP%29" title=" blood pressure (BP)"> blood pressure (BP)</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate" title=" heart rate"> heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20%28ECG%29" title=" electrocardiogram (ECG)"> electrocardiogram (ECG)</a> </p> <a href="https://publications.waset.org/abstracts/26470/remote-wireless-patient-monitoring-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1030</span> Detection of Cardiac Arrhythmia Using Principal Component Analysis and Xgboost Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujay%20Kotwale">Sujay Kotwale</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramasubba%20Reddy%20M."> Ramasubba Reddy M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiac%20arrhythmia" title="cardiac arrhythmia">cardiac arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title=" XGBoost"> XGBoost</a> </p> <a href="https://publications.waset.org/abstracts/126916/detection-of-cardiac-arrhythmia-using-principal-component-analysis-and-xgboost-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1029</span> A New Framework for ECG Signal Modeling and Compression Based on Compressed Sensing Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Eftekharifar">Siavash Eftekharifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Yousefi%20Rezaii"> Tohid Yousefi Rezaii</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shamsi"> Mahdi Shamsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20compression" title=" ECG compression"> ECG compression</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20kernel" title=" Gaussian kernel"> Gaussian kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/31469/a-new-framework-for-ecg-signal-modeling-and-compression-based-on-compressed-sensing-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1028</span> Auto Classification of Multiple ECG Arrhythmic Detection via Machine Learning Techniques: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ng%20Liang%20Shen">Ng Liang Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Hau%20Yuan%20Wen"> Hau Yuan Wen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=QRS" title=" QRS"> QRS</a> </p> <a href="https://publications.waset.org/abstracts/58871/auto-classification-of-multiple-ecg-arrhythmic-detection-via-machine-learning-techniques-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58871.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1027</span> Compact Low-Voltage Biomedical Instrumentation Amplifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phanumas%20Khumsat">Phanumas Khumsat</a>, <a href="https://publications.waset.org/abstracts/search?q=Chalermchai%20Janmane"> Chalermchai Janmane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low-voltage instrumentation amplifier has been proposed for 3-lead electrocardiogram measurement system. The circuit’s interference rejection technique is based upon common-mode feed-forwarding where common-mode currents have cancelled each other at the output nodes. The common-mode current for cancellation is generated by means of common-mode sensing and emitter or source followers with resistors employing only one transistor. Simultaneously this particular transistor also provides common-mode feedback to the patient’s right/left leg to further reduce interference entering the amplifier. The proposed designs have been verified with simulations in 0.18-µm CMOS process operating under 1.0-V supply with CMRR greater than 80dB. Moreover ECG signals have experimentally recorded with the proposed instrumentation amplifiers implemented from discrete BJT (BC547, BC558) and MOSFET (ALD1106, ALD1107) transistors working with 1.5-V supply. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=common-mode%20feedback" title=" common-mode feedback"> common-mode feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=common-mode%20feedforward" title=" common-mode feedforward"> common-mode feedforward</a>, <a href="https://publications.waset.org/abstracts/search?q=communication%20engineering" title=" communication engineering"> communication engineering</a> </p> <a href="https://publications.waset.org/abstracts/4913/compact-low-voltage-biomedical-instrumentation-amplifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4913.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1026</span> Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Jianhong">Xiang Jianhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Cong"> Wang Cong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Linyu"> Wang Linyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title="telemedicine">telemedicine</a>, <a href="https://publications.waset.org/abstracts/search?q=fetal%20ECG" title=" fetal ECG"> fetal ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20sparse%20reconstruction" title=" joint sparse reconstruction"> joint sparse reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20sparse%20signal" title=" block sparse signal"> block sparse signal</a> </p> <a href="https://publications.waset.org/abstracts/154614/compressed-sensing-of-fetal-electrocardiogram-signals-based-on-joint-block-multi-orthogonal-least-squares-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1025</span> Denoising Convolutional Neural Network Assisted Electrocardiogram Signal Watermarking for Secure Transmission in E-Healthcare Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Rani">Jyoti Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashima%20Anand"> Ashima Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivendra%20Shivani"> Shivendra Shivani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, physiological signals obtained in telemedicine have been stored independently from patient information. In addition, people have increasingly turned to mobile devices for information on health-related topics. Major authentication and security issues may arise from this storing, degrading the reliability of diagnostics. This study introduces an approach to reversible watermarking, which ensures security by utilizing the electrocardiogram (ECG) signal as a carrier for embedding patient information. In the proposed work, Pan-Tompkins++ is employed to convert the 1D ECG signal into a 2D signal. The frequency subbands of a signal are extracted using RDWT(Redundant discrete wavelet transform), and then one of the subbands is subjected to MSVD (Multiresolution singular valued decomposition for masking. Finally, the encrypted watermark is embedded within the signal. The experimental results show that the watermarked signal obtained is indistinguishable from the original signals, ensuring the preservation of all diagnostic information. In addition, the DnCNN (Denoising convolutional neural network) concept is used to denoise the retrieved watermark for improved accuracy. The proposed ECG signal-based watermarking method is supported by experimental results and evaluations of its effectiveness. The results of the robustness tests demonstrate that the watermark is susceptible to the most prevalent watermarking attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG" title="ECG">ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=VMD" title=" VMD"> VMD</a>, <a href="https://publications.waset.org/abstracts/search?q=watermarking" title=" watermarking"> watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=PanTompkins%2B%2B" title=" PanTompkins++"> PanTompkins++</a>, <a href="https://publications.waset.org/abstracts/search?q=RDWT" title=" RDWT"> RDWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DnCNN" title=" DnCNN"> DnCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=MSVD" title=" MSVD"> MSVD</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20encryption" title=" chaotic encryption"> chaotic encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=attacks" title=" attacks"> attacks</a> </p> <a href="https://publications.waset.org/abstracts/174732/denoising-convolutional-neural-network-assisted-electrocardiogram-signal-watermarking-for-secure-transmission-in-e-healthcare-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174732.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1024</span> Robust Heart Rate Estimation from Multiple Cardiovascular and Non-Cardiovascular Physiological Signals Using Signal Quality Indices and Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shalini%20Rankawat">Shalini Rankawat</a>, <a href="https://publications.waset.org/abstracts/search?q=Mansi%20Rankawat"> Mansi Rankawat</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Dubey"> Rahul Dubey</a>, <a href="https://publications.waset.org/abstracts/search?q=Mazad%20Zaveri"> Mazad Zaveri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Physiological signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often seriously corrupted by noise, artifacts, and missing data, which lead to errors in the estimation of heart rate (HR) and incidences of false alarm from ICU monitors. Clinical support in ICU requires most reliable heart rate estimation. Cardiac activity, because of its relatively high electrical energy, may introduce artifacts in Electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG) recordings. This paper presents a robust heart rate estimation method by detection of R-peaks of ECG artifacts in EEG, EMG & EOG signals, using energy-based function and a novel Signal Quality Index (SQI) assessment technique. SQIs of physiological signals (EEG, EMG, & EOG) were obtained by correlation of nonlinear energy operator (teager energy) of these signals with either ECG or ABP signal. HR is estimated from ECG, ABP, EEG, EMG, and EOG signals from separate Kalman filter based upon individual SQIs. Data fusion of each HR estimate was then performed by weighing each estimate by the Kalman filters’ SQI modified innovations. The fused signal HR estimate is more accurate and robust than any of the individual HR estimate. This method was evaluated on MIMIC II data base of PhysioNet from bedside monitors of ICU patients. The method provides an accurate HR estimate even in the presence of noise and artifacts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG" title="ECG">ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ABP" title=" ABP"> ABP</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=EMG" title=" EMG"> EMG</a>, <a href="https://publications.waset.org/abstracts/search?q=EOG" title=" EOG"> EOG</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20artifacts" title=" ECG artifacts"> ECG artifacts</a>, <a href="https://publications.waset.org/abstracts/search?q=Teager-Kaiser%20energy" title=" Teager-Kaiser energy"> Teager-Kaiser energy</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate" title=" heart rate"> heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20quality%20index" title=" signal quality index"> signal quality index</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a> </p> <a href="https://publications.waset.org/abstracts/17506/robust-heart-rate-estimation-from-multiple-cardiovascular-and-non-cardiovascular-physiological-signals-using-signal-quality-indices-and-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17506.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">696</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1023</span> Analysis of a IncResU-Net Model for R-Peak Detection in ECG Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Beatriz%20Lafuente%20Alc%C3%A1zar">Beatriz Lafuente Alcázar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yash%20Wani"> Yash Wani</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20J.%20Nimunkar"> Amit J. Nimunkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmia" title="arrhythmia">arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=R-peaks" title=" R-peaks"> R-peaks</a> </p> <a href="https://publications.waset.org/abstracts/153003/analysis-of-a-incresu-net-model-for-r-peak-detection-in-ecg-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1022</span> A Quality Index Optimization Method for Non-Invasive Fetal ECG Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lucia%20Billeci">Lucia Billeci</a>, <a href="https://publications.waset.org/abstracts/search?q=Gennaro%20Tartarisco"> Gennaro Tartarisco</a>, <a href="https://publications.waset.org/abstracts/search?q=Maurizio%20Varanini"> Maurizio Varanini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fetal%20electrocardiography" title="fetal electrocardiography">fetal electrocardiography</a>, <a href="https://publications.waset.org/abstracts/search?q=fetal%20QRS%20detection" title=" fetal QRS detection"> fetal QRS detection</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis%20%28ICA%29" title=" independent component analysis (ICA)"> independent component analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=wearable" title=" wearable"> wearable</a> </p> <a href="https://publications.waset.org/abstracts/51208/a-quality-index-optimization-method-for-non-invasive-fetal-ecg-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1021</span> An Auxiliary Technique for Coronary Heart Disease Prediction by Analyzing Electrocardiogram Based on ResNet and Bi-Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Zhang">Yang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20He"> Jian He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title="Bi-LSTM">Bi-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CHD" title=" CHD"> CHD</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%C2%A0window" title=" sliding window"> sliding window</a> </p> <a href="https://publications.waset.org/abstracts/165165/an-auxiliary-technique-for-coronary-heart-disease-prediction-by-analyzing-electrocardiogram-based-on-resnet-and-bi-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165165.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1020</span> Portable Cardiac Monitoring System Based on Real-Time Microcontroller and Multiple Communication Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ionel%20Zagan">Ionel Zagan</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasile%20Gheorghita%20Gaitan"> Vasile Gheorghita Gaitan</a>, <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Brezulianu"> Adrian Brezulianu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the contributions in designing a mobile system named Tele-ECG implemented for remote monitoring of cardiac patients. For a better flexibility of this application, the authors chose to implement a local memory and multiple communication interfaces. The project described in this presentation is based on the ARM Cortex M0+ microcontroller and the ADAS1000 dedicated chip necessary for the collection and transmission of Electrocardiogram signals (ECG) from the patient to the microcontroller, without altering the performances and the stability of the system. The novelty brought by this paper is the implementation of a remote monitoring system for cardiac patients, having a real-time behavior and multiple interfaces. The microcontroller is responsible for processing digital signals corresponding to ECG and also for the implementation of communication interface with the main server, using GSM/Bluetooth SIMCOM SIM800C module. This paper translates all the characteristics of the Tele-ECG project representing a feasible implementation in the biomedical field. Acknowledgment: This paper was supported by the project 'Development and integration of a mobile tele-electrocardiograph in the GreenCARDIO© system for patients monitoring and diagnosis - m-GreenCARDIO', Contract no. BG58/30.09.2016, PNCDI III, Bridge Grant 2016, using the infrastructure from the project 'Integrated Center for research, development and innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for fabrication and control', Contract No. 671/09.04.2015, Sectoral Operational Program for Increase of the Economic Competitiveness co-funded from the European Regional Development Fund. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tele-ECG" title="Tele-ECG">Tele-ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20cardiac%20monitoring" title=" real-time cardiac monitoring"> real-time cardiac monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=microcontroller" title=" microcontroller"> microcontroller</a> </p> <a href="https://publications.waset.org/abstracts/62189/portable-cardiac-monitoring-system-based-on-real-time-microcontroller-and-multiple-communication-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1019</span> Analysis of Nonlinear and Non-Stationary Signal to Extract the Features Using Hilbert Huang Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20N.%20Paithane">A. N. Paithane</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20S.%20Bormane"> D. S. Bormane</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20D.%20Shirbahadurkar"> S. D. Shirbahadurkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrinsic%20mode%20function%20%28IMF%29" title="intrinsic mode function (IMF)">intrinsic mode function (IMF)</a>, <a href="https://publications.waset.org/abstracts/search?q=Hilbert-Huang%20transform%20%28HHT%29" title=" Hilbert-Huang transform (HHT)"> Hilbert-Huang transform (HHT)</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition%20%28EMD%29" title=" empirical mode decomposition (EMD)"> empirical mode decomposition (EMD)</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20%28ECG%29" title=" electrocardiogram (ECG)"> electrocardiogram (ECG)</a> </p> <a href="https://publications.waset.org/abstracts/19551/analysis-of-nonlinear-and-non-stationary-signal-to-extract-the-features-using-hilbert-huang-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19551.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">580</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1018</span> Additive White Gaussian Noise Filtering from ECG by Wiener Filter and Median Filter: A Comparative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Javidnia">Hossein Javidnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Salehe%20Taheri"> Salehe Taheri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Electrocardiogram (ECG) is the recording of the heart’s electrical potential versus time. ECG signals are often contaminated with noise such as baseline wander and muscle noise. As these signals have been widely used in clinical studies to detect heart diseases, it is essential to filter these noises. In this paper we compare performance of Wiener Filtering and Median Filtering methods to filter Additive White Gaussian (AWG) noise with the determined signal to noise ratio (SNR) ranging from 3 to 5 dB applied to long-term ECG recordings samples. Root mean square error (RMSE) and coefficient of determination (R2) between the filtered ECG and original ECG was used as the filter performance indicator. Experimental results show that Wiener filter has better noise filtering performance than Median filter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20noise%20filtering" title="ECG noise filtering">ECG noise filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20filtering" title=" Wiener filtering"> Wiener filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=median%20filtering" title=" median filtering"> median filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20noise" title=" Gaussian noise"> Gaussian noise</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering%20performance" title=" filtering performance"> filtering performance</a> </p> <a href="https://publications.waset.org/abstracts/9623/additive-white-gaussian-noise-filtering-from-ecg-by-wiener-filter-and-median-filter-a-comparative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9623.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1017</span> Recent Advancement in Fetal Electrocardiogram Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Savita">Savita</a>, <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Sharma"> Anurag Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsukhpreet%20Singh"> Harsukhpreet Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fetal Electrocardiogram (fECG) is a widely used technique to assess the fetal well-being and identify any changes that might be with problems during pregnancy and to evaluate the health and conditions of the fetus. Various techniques or methods have been employed to diagnose the fECG from abdominal signal. This paper describes the facile approach for the estimation of the fECG known as Adaptive Comb. Filter (ACF). The ACF can adjust according to the temporal variations in fundamental frequency by itself that used for the estimation of the quasi periodic signal of ECG signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aECG" title="aECG">aECG</a>, <a href="https://publications.waset.org/abstracts/search?q=ACF" title=" ACF"> ACF</a>, <a href="https://publications.waset.org/abstracts/search?q=fECG" title=" fECG"> fECG</a>, <a href="https://publications.waset.org/abstracts/search?q=mECG" title=" mECG"> mECG</a> </p> <a href="https://publications.waset.org/abstracts/49031/recent-advancement-in-fetal-electrocardiogram-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1016</span> Effects of Acute Exposure to WIFI Signals (2,45 GHz) on Heart Variability and Blood Pressure in Albinos Rabbit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linda%20Saili">Linda Saili</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Hanini"> Amel Hanini</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiraz%20Smirani"> Chiraz Smirani</a>, <a href="https://publications.waset.org/abstracts/search?q=Iness%20Azzouz"> Iness Azzouz</a>, <a href="https://publications.waset.org/abstracts/search?q=Amina%20Azzouz"> Amina Azzouz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafedh%20Abdemelek"> Hafedh Abdemelek</a>, <a href="https://publications.waset.org/abstracts/search?q=Zihad%20Bouslama"> Zihad Bouslama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram and arterial pressure measurements were studied under acute exposures to WIFI (2.45 GHz) during one hour in adult male rabbits. Antennas of WIFI were placed at 25 cm at the right side near the heart. Acute exposure of rabbits to WIFI increased heart frequency (+ 22%) and arterial blood pressure (+14%). Moreover, analysis of ECG revealed that WIFI induced a combined increase of PR and QT intervals. By contrast, the same exposure failed to alter the maximum amplitude and P waves. After intravenously injection of dopamine (0.50 ml/kg) and epinephrine (0.50ml/kg) under acute exposure to RF we found that WIFI alter catecholamines(dopamine, epinephrine) action on heart variability and blood pressure compared to control. These results suggest for the first time, as far as we know, that exposure to WIFI affect heart rhythm, blood pressure, and catecholamines efficacy on cardiovascular system; indicating that radio frequency can act directly and/or indirectly on the cardiovascular system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20%28HR%29" title="heart rate (HR)">heart rate (HR)</a>, <a href="https://publications.waset.org/abstracts/search?q=arterial%20pressure%20%28PA%29" title=" arterial pressure (PA)"> arterial pressure (PA)</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20%28ECG%29" title=" electrocardiogram (ECG)"> electrocardiogram (ECG)</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20efficacy%20of%0D%0Acatecholamines" title=" the efficacy of catecholamines"> the efficacy of catecholamines</a>, <a href="https://publications.waset.org/abstracts/search?q=dopamine" title=" dopamine"> dopamine</a>, <a href="https://publications.waset.org/abstracts/search?q=epinephrine" title=" epinephrine"> epinephrine</a> </p> <a href="https://publications.waset.org/abstracts/40803/effects-of-acute-exposure-to-wifi-signals-245-ghz-on-heart-variability-and-blood-pressure-in-albinos-rabbit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40803.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1015</span> Secured Embedding of Patient’s Confidential Data in Electrocardiogram Using Chaotic Maps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Butta%20Singh">Butta Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20maps" title="chaotic maps">chaotic maps</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20steganography" title=" ECG steganography"> ECG steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20embedding" title=" data embedding"> data embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a> </p> <a href="https://publications.waset.org/abstracts/78602/secured-embedding-of-patients-confidential-data-in-electrocardiogram-using-chaotic-maps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1014</span> Signals Monitored During Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot%20McGrath">Launcelot McGrath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20signals" title="biological signals">biological signals</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20acquisition" title=" signal acquisition"> signal acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesiology" title=" anaesthesiology"> anaesthesiology</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20monitoring" title=" patient monitoring"> patient monitoring</a> </p> <a href="https://publications.waset.org/abstracts/158784/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158784.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1013</span> Wireless Based System for Continuous Electrocardiography Monitoring during Surgery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Bensafia">K. Bensafia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mansour"> A. Mansour</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Le%20Maillot"> G. Le Maillot</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Clement"> B. Clement</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Reynet"> O. Reynet</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Ari%C3%A8s"> P. Ariès</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Haddab"> S. Haddab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a system designed for wireless acquisition, the recording of electrocardiogram (ECG) signals and the monitoring of the heart’s health during surgery. This wireless recording system allows us to visualize and monitor the state of the heart’s health during a surgery, even if the patient is moved from the operating theater to post anesthesia care unit. The acquired signal is transmitted via a Bluetooth unit to a PC where the data are displayed, stored and processed. To test the reliability of our system, a comparison between ECG signals processed by a conventional ECG monitoring system (Datex-Ohmeda) and by our wireless system is made. The comparison is based on the shape of the ECG signal, the duration of the QRS complex, the P and T waves, as well as the position of the ST segments with respect to the isoelectric line. The proposed system is presented and discussed. The results have confirmed that the use of Bluetooth during surgery does not affect the devices used and vice versa. Pre- and post-processing steps are briefly discussed. Experimental results are also provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiography" title="electrocardiography">electrocardiography</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20system" title=" wireless system"> wireless system</a> </p> <a href="https://publications.waset.org/abstracts/79677/wireless-based-system-for-continuous-electrocardiography-monitoring-during-surgery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79677.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1012</span> A Pole Radius Varying Notch Filter with Transient Suppression for Electrocardiogram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramesh%20Rajagopalan">Ramesh Rajagopalan</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Dahlstrom"> Adam Dahlstrom</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Noise removal techniques play a vital role in the performance of electrocardiographic (ECG) signal processing systems. ECG signals can be corrupted by various kinds of noise such as baseline wander noise, electromyographic interference, and power-line interference. One of the significant challenges in ECG signal processing is the degradation caused by additive 50 or 60 Hz power-line interference. This work investigates the removal of power line interference and suppression of transient response for filtering noise corrupted ECG signals. We demonstrate the effectiveness of Infinite Impulse Response (IIR) notch filter with time varying pole radius for improving the transient behavior. The temporary change in the pole radius of the filter diminishes the transient behavior. Simulation results show that the proposed IIR filter with time varying pole radius outperforms traditional IIR notch filters in terms of mean square error and transient suppression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=notch%20filter" title="notch filter">notch filter</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=transient" title=" transient"> transient</a>, <a href="https://publications.waset.org/abstracts/search?q=pole%20radius" title=" pole radius"> pole radius</a> </p> <a href="https://publications.waset.org/abstracts/5982/a-pole-radius-varying-notch-filter-with-transient-suppression-for-electrocardiogram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5982.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">377</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1011</span> Classifications of Sleep Apnea (Obstructive, Central, Mixed) and Hypopnea Events Using Wavelet Packet Transform and Support Vector Machines (VSM)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benghenia%20Hadj%20Abd%20El%20Kader">Benghenia Hadj Abd El Kader</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sleep apnea events as obstructive, central, mixed or hypopnea are characterized by frequent breathing cessations or reduction in upper airflow during sleep. An advanced method for analyzing the patterning of biomedical signals to recognize obstructive sleep apnea and hypopnea is presented. In the aim to extract characteristic parameters, which will be used for classifying the above stated (obstructive, central, mixed) sleep apnea and hypopnea, the proposed method is based first on the analysis of polysomnography signals such as electrocardiogram signal (ECG) and electromyogram (EMG), then classification of the (obstructive, central, mixed) sleep apnea and hypopnea. The analysis is carried out using the wavelet transform technique in order to extract characteristic parameters whereas classification is carried out by applying the SVM (support vector machine) technique. The obtained results show good recognition rates using characteristic parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=obstructive" title="obstructive">obstructive</a>, <a href="https://publications.waset.org/abstracts/search?q=central" title=" central"> central</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed" title=" mixed"> mixed</a>, <a href="https://publications.waset.org/abstracts/search?q=sleep%20apnea" title=" sleep apnea"> sleep apnea</a>, <a href="https://publications.waset.org/abstracts/search?q=hypopnea" title=" hypopnea"> hypopnea</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=EMG" title=" EMG"> EMG</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title=" SVM classifier"> SVM classifier</a> </p> <a href="https://publications.waset.org/abstracts/50100/classifications-of-sleep-apnea-obstructive-central-mixed-and-hypopnea-events-using-wavelet-packet-transform-and-support-vector-machines-vsm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50100.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">371</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1010</span> Combined Odd Pair Autoregressive Coefficients for Epileptic EEG Signals Classification by Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukari%20Nassim">Boukari Nassim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signals%20classification" title=" EEG signals classification"> EEG signals classification</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20odd%20pair%20autoregressive%20coefficients" title=" combined odd pair autoregressive coefficients"> combined odd pair autoregressive coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a> </p> <a href="https://publications.waset.org/abstracts/47454/combined-odd-pair-autoregressive-coefficients-for-epileptic-eeg-signals-classification-by-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1009</span> FlexPoints: Efficient Algorithm for Detection of Electrocardiogram Characteristic Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Bulanda">Daniel Bulanda</a>, <a href="https://publications.waset.org/abstracts/search?q=Janusz%20A.%20Starzyk"> Janusz A. Starzyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Horzyk"> Adrian Horzyk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=characteristic%20points" title="characteristic points">characteristic points</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20compression" title=" signal compression"> signal compression</a> </p> <a href="https://publications.waset.org/abstracts/132090/flexpoints-efficient-algorithm-for-detection-of-electrocardiogram-characteristic-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1008</span> Theoretical BER Analyzing of MPSK Signals Based on the Signal Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Qing-feng">Jing Qing-feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Danmei"> Liu Danmei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on the optimum detection, signal projection and Maximum A Posteriori (MAP) rule, Proakis has deduced the theoretical BER equation of Gray coded MPSK signals. Proakis analyzed the BER theoretical equations mainly based on the projection of signals, which is difficult to be understood. This article solve the same problem based on the signal space, which explains the vectors relations among the sending signals, received signals and noises. The more explicit and easy-deduced process is illustrated in this article based on the signal space, which can illustrated the relations among the signals and noises clearly. This kind of deduction has a univocal geometry meaning. It can explain the correlation between the production and calculation of BER in vector level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MPSK" title="MPSK">MPSK</a>, <a href="https://publications.waset.org/abstracts/search?q=MAP" title=" MAP"> MAP</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20space" title=" signal space"> signal space</a>, <a href="https://publications.waset.org/abstracts/search?q=BER" title="BER">BER</a> </p> <a href="https://publications.waset.org/abstracts/45896/theoretical-ber-analyzing-of-mpsk-signals-based-on-the-signal-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1007</span> Early Warning Signals: Role and Status of Risk Management in Small and Medium Enterprises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Kel%C3%AD%C5%A1ek">Alexander Kelíšek</a>, <a href="https://publications.waset.org/abstracts/search?q=Denisa%20Janasov%C3%A1"> Denisa Janasová</a>, <a href="https://publications.waset.org/abstracts/search?q=Veronika%20Mita%C5%A1ov%C3%A1"> Veronika Mitašová</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weak signals using is often associated with early warning. It is possible to find a link between early warning, respectively early problems detection and risk management. The idea of early warning is very important in the context of crisis management because of the risk prevention possibility. Weak signals are likened to risk symptoms. Nowadays, their usefulness as a tool of proactive problems solving is emphasized. Based on it, it is possible to use weak signals not only in strategic planning, project management, or early warning system, but also as a subsidiary element in risk management. The main question is how to effectively integrate weak signals into risk management. The main aim of the paper is to point out the possibilities of weak signals using in small and medium enterprises risk management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=early%20warning%20system" title="early warning system">early warning system</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20signals" title=" weak signals"> weak signals</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20and%20medium%20enterprises%20%28SMEs%29" title=" small and medium enterprises (SMEs)"> small and medium enterprises (SMEs)</a> </p> <a href="https://publications.waset.org/abstracts/59504/early-warning-signals-role-and-status-of-risk-management-in-small-and-medium-enterprises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1006</span> Signals Monitored during Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot.McGrath">Launcelot.McGrath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Biosignal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20biosignals" title="general biosignals">general biosignals</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesia" title=" anaesthesia"> anaesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=biological" title=" biological"> biological</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a> </p> <a href="https://publications.waset.org/abstracts/158537/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158537.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1005</span> A Physiological Approach for Early Detection of Hemorrhage </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabie%20Fadil">Rabie Fadil</a>, <a href="https://publications.waset.org/abstracts/search?q=Parshuram%20Aarotale"> Parshuram Aarotale</a>, <a href="https://publications.waset.org/abstracts/search?q=Shubha%20Majumder"> Shubha Majumder</a>, <a href="https://publications.waset.org/abstracts/search?q=Bijay%20Guargain"> Bijay Guargain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 ± 6 year, weight:73 ± 11 kg, height:172 ± 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85±4 bpm vs 68 ± 5bpm, p < 0.01), ST (191 ± 10 ms vs 253 ± 31 ms, p < 0.05), LVET (350 ± 14 ms vs 479 ± 47 ms, p < 0.05) and DT (551 ± 22 ms vs 683 ± 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20pressure" title="blood pressure">blood pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=hemorrhage" title=" hemorrhage"> hemorrhage</a>, <a href="https://publications.waset.org/abstracts/search?q=lower-body%20negative%20pressure" title=" lower-body negative pressure"> lower-body negative pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=LBNP" title=" LBNP"> LBNP</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning "> machine learning </a> </p> <a href="https://publications.waset.org/abstracts/114872/a-physiological-approach-for-early-detection-of-hemorrhage" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20signals&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20signals&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=electrocardiogram%20signals&page=4">4</a></li> <li class="page-item"><a class="page-link" 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